A Class of Photometric Invariants:
Separating Material from Shape and Illumination

We derive a new class of photometric invariants that can be used for a
variety of vision tasks including lighting invariant material
segmentation, change detection and tracking, as well as material
invariant shape recognition. The key idea is the formulation of a
scene radiance model for the class of ``separable'' BRDFs, that can be
decomposed into material related terms and object shape and lighting
related terms. All the proposed invariants are simple rational
functions of the appearance parameters (say, material or shape and
lighting). The invariants in this class differ from one another in
the number and type of image measurements they require. Most of the
invariants in this class need changes in illumination or object
position between image acquisitions. The invariants can handle large
changes in lighting which pose problems for most existing vision
algorithms. We demonstrate the power of these invariants using scenes
with complex shapes, materials, textures, shadows and specularities.